In this module, we will delve into the world of optical remote sensing data, starting with the fundamental principles that govern its collection. You'll examine the different types of optical data and how they are used, particularly focusing on Landsat data. Additionally, you'll explore the specifics of Landsat sensors and gain hands-on experience in using QGIS to download and view this data. By the end of this section, you'll be equipped with the knowledge and skills needed to work with optical remote sensing data in your analyses.

In this module, we will focus on the crucial steps involved in pre-processing optical remote sensing data. You'll learn why pre-processing is essential, particularly for improving data accuracy. The module will guide you through performing atmospheric correction on Landsat data using R, and introduce you to the Semi-Automatic Classification Plugin in QGIS for efficient pre-processing. Additionally, you'll assess the quality of atmospherically corrected outputs and explore the practical applications of pre-processed data. By the end of this section, you'll have the skills to refine raw satellite data for meaningful analysis.

In this module, we will explore the diverse applications of optical remote sensing data across various analytical processes. You'll begin by mastering band manipulation in QGIS, followed by the application of band math to derive critical insights. The module will introduce you to texture indices and tasseled cap transformations, offering both theoretical knowledge and practical implementation using GRASS GIS and ESA SNAP. Additionally, you'll delve into vegetation indices and learn how to reduce data dimensionality for more efficient analysis. By the end of this section, you'll be well-versed in multiple advanced techniques for leveraging optical data in your projects

In this module, we will delve into the classification of remote sensing satellite data, covering both unsupervised and supervised methods. You’ll begin by exploring the theory behind these approaches, followed by practical applications using ESA SNAP and QGIS. The module also introduces machine learning concepts and their integration into remote sensing classification, guiding you through creating training data and applying advanced algorithms to satellite imagery. By the end of this section, you’ll be equipped with comprehensive skills to classify and analyze remote sensing data accurately and efficiently

In this module, we will explore active remote sensing data, focusing on Synthetic Aperture Radar (SAR). You'll begin by understanding the reasons for using active remote sensing over passive methods, with a particular emphasis on SAR technology. The module will guide you through the process of obtaining ALOS PALSAR data and applying essential pre-processing steps. You'll also learn to filter speckles from SAR imagery to improve data quality, and finally, you'll extract back-scatter values, a critical step for interpreting SAR data. By the end of this section, you'll have a solid foundation in working with active remote-sensing data